Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	32.100641
daal4py_KNeighborsClassifier	0.0	5.0	20.274588
KNeighborsClassifier_kd_tree	0.0	5.0	46.531391
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	44.499347
KMeans_tall	0.0	1.0	42.518739
daal4py_KMeans_tall	0.0	1.0	19.551095
KMeans_short	0.0	0.0	23.062622
daal4py_KMeans_short	0.0	0.0	13.428654
LogisticRegression	0.0	1.0	22.295667
daal4py_LogisticRegression	0.0	1.0	12.816701
Ridge	0.0	0.0	25.263209
daal4py_Ridge	0.0	0.0	8.460757
total	0.0	33.0	10.885282

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.132	0.005	1000000	1000000	100	brute	-1	1	6.079	NaN	0.978	0.984	0.486	0.006	0.271	0.012	See
1	KNeighborsClassifier	predict	29.570	0.301	1000000	1000	100	brute	-1	1	0.000	0.030	0.978	0.984	3.712	0.015	7.966	0.087	See
2	KNeighborsClassifier	predict	0.195	0.018	1000000	1	100	brute	-1	1	0.004	0.000	0.978	0.984	0.101	0.002	1.928	0.180	See
3	KNeighborsClassifier	fit	0.124	0.003	1000000	1000000	100	brute	-1	5	6.446	NaN	0.978	0.984	0.488	0.007	0.254	0.006	See
4	KNeighborsClassifier	predict	35.565	0.000	1000000	1000	100	brute	-1	5	0.000	0.036	0.978	0.984	3.733	0.022	9.526	0.055	See
5	KNeighborsClassifier	predict	0.196	0.014	1000000	1	100	brute	-1	5	0.004	0.000	0.978	0.984	0.101	0.003	1.939	0.150	See
6	KNeighborsClassifier	fit	0.124	0.006	1000000	1000000	100	brute	-1	100	6.431	NaN	0.978	0.984	0.484	0.023	0.257	0.017	See
7	KNeighborsClassifier	predict	36.041	0.000	1000000	1000	100	brute	-1	100	0.000	0.036	0.978	0.984	3.798	0.028	9.489	0.069	See
8	KNeighborsClassifier	predict	0.201	0.013	1000000	1	100	brute	-1	100	0.004	0.000	0.978	0.984	0.101	0.003	1.985	0.134	See
9	KNeighborsClassifier	fit	0.126	0.004	1000000	1000000	100	brute	1	1	6.349	NaN	0.978	0.984	0.485	0.010	0.260	0.010	See
10	KNeighborsClassifier	predict	15.004	0.158	1000000	1000	100	brute	1	1	0.000	0.015	0.978	0.984	3.720	0.024	4.034	0.050	See
11	KNeighborsClassifier	predict	0.199	0.008	1000000	1	100	brute	1	1	0.004	0.000	0.978	0.984	0.099	0.003	2.005	0.099	See
12	KNeighborsClassifier	fit	0.125	0.004	1000000	1000000	100	brute	1	5	6.404	NaN	0.978	0.984	0.481	0.011	0.260	0.010	See
13	KNeighborsClassifier	predict	22.234	0.048	1000000	1000	100	brute	1	5	0.000	0.022	0.978	0.984	3.704	0.022	6.003	0.038	See
14	KNeighborsClassifier	predict	0.216	0.006	1000000	1	100	brute	1	5	0.004	0.000	0.978	0.984	0.099	0.004	2.176	0.102	See
15	KNeighborsClassifier	fit	0.123	0.005	1000000	1000000	100	brute	1	100	6.498	NaN	0.978	0.984	0.475	0.012	0.259	0.012	See
16	KNeighborsClassifier	predict	24.376	0.436	1000000	1000	100	brute	1	100	0.000	0.024	0.978	0.984	3.793	0.025	6.426	0.123	See
17	KNeighborsClassifier	predict	0.222	0.010	1000000	1	100	brute	1	100	0.004	0.000	0.978	0.984	0.100	0.005	2.214	0.157	See
18	KNeighborsClassifier	fit	0.066	0.003	1000000	1000000	2	brute	-1	1	0.242	NaN	0.978	0.984	0.094	0.002	0.703	0.039	See
19	KNeighborsClassifier	predict	24.741	0.019	1000000	1000	2	brute	-1	1	0.000	0.025	0.978	0.984	0.780	0.010	31.738	0.420	See
20	KNeighborsClassifier	predict	0.021	0.002	1000000	1	2	brute	-1	1	0.001	0.000	0.978	0.984	0.004	0.000	5.018	0.801	See
21	KNeighborsClassifier	fit	0.058	0.001	1000000	1000000	2	brute	-1	5	0.274	NaN	0.978	0.984	0.095	0.006	0.612	0.037	See
22	KNeighborsClassifier	predict	31.716	0.000	1000000	1000	2	brute	-1	5	0.000	0.032	0.978	0.984	0.790	0.015	40.131	0.745	See
23	KNeighborsClassifier	predict	0.031	0.002	1000000	1	2	brute	-1	5	0.001	0.000	0.978	0.984	0.004	0.001	7.499	1.357	See
24	KNeighborsClassifier	fit	0.061	0.003	1000000	1000000	2	brute	-1	100	0.264	NaN	0.978	0.984	0.093	0.001	0.653	0.034	See
25	KNeighborsClassifier	predict	30.508	0.000	1000000	1000	2	brute	-1	100	0.000	0.031	0.978	0.984	0.846	0.005	36.049	0.217	See
26	KNeighborsClassifier	predict	0.029	0.003	1000000	1	2	brute	-1	100	0.001	0.000	0.978	0.984	0.004	0.001	6.866	1.083	See
27	KNeighborsClassifier	fit	0.053	0.001	1000000	1000000	2	brute	1	1	0.303	NaN	0.978	0.984	0.096	0.005	0.551	0.027	See
28	KNeighborsClassifier	predict	10.460	0.039	1000000	1000	2	brute	1	1	0.000	0.010	0.978	0.984	0.790	0.011	13.247	0.189	See
29	KNeighborsClassifier	predict	0.014	0.001	1000000	1	2	brute	1	1	0.001	0.000	0.978	0.984	0.004	0.000	3.277	0.404	See
30	KNeighborsClassifier	fit	0.054	0.003	1000000	1000000	2	brute	1	5	0.295	NaN	0.978	0.984	0.093	0.002	0.581	0.029	See
31	KNeighborsClassifier	predict	17.235	0.042	1000000	1000	2	brute	1	5	0.000	0.017	0.978	0.984	0.785	0.008	21.950	0.233	See
32	KNeighborsClassifier	predict	0.023	0.003	1000000	1	2	brute	1	5	0.001	0.000	0.978	0.984	0.005	0.001	4.834	1.466	See
33	KNeighborsClassifier	fit	0.053	0.002	1000000	1000000	2	brute	1	100	0.299	NaN	0.978	0.984	0.098	0.006	0.548	0.040	See
34	KNeighborsClassifier	predict	17.326	0.093	1000000	1000	2	brute	1	100	0.000	0.017	0.978	0.984	0.859	0.012	20.181	0.302	See
35	KNeighborsClassifier	predict	0.023	0.002	1000000	1	2	brute	1	100	0.001	0.000	0.978	0.984	0.004	0.000	5.458	0.784	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	2.579	0.023	1000000	1000000	10	kd_tree	-1	1	0.031	NaN	0.983	0.984	0.719	0.011	3.586	0.065	See
1	KNeighborsClassifier_kd_tree	predict	0.427	0.013	1000000	1000	10	kd_tree	-1	1	0.000	0.000	0.983	0.984	0.120	0.014	3.552	0.435	See
2	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	1	0.026	0.000	0.983	0.984	0.000	0.000	10.885	5.500	See
3	KNeighborsClassifier_kd_tree	fit	2.581	0.023	1000000	1000000	10	kd_tree	-1	5	0.031	NaN	0.983	0.984	0.727	0.012	3.548	0.067	See
4	KNeighborsClassifier_kd_tree	predict	0.790	0.012	1000000	1000	10	kd_tree	-1	5	0.000	0.001	0.983	0.984	0.222	0.008	3.566	0.143	See
5	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	5	0.024	0.000	0.983	0.984	0.001	0.000	6.371	3.409	See
6	KNeighborsClassifier_kd_tree	fit	2.688	0.087	1000000	1000000	10	kd_tree	-1	100	0.030	NaN	0.983	0.984	0.686	0.014	3.916	0.151	See
7	KNeighborsClassifier_kd_tree	predict	2.798	0.023	1000000	1000	10	kd_tree	-1	100	0.000	0.003	0.983	0.984	0.645	0.008	4.340	0.064	See
8	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	-1	100	0.018	0.000	0.983	0.984	0.001	0.000	5.579	2.125	See
9	KNeighborsClassifier_kd_tree	fit	2.769	0.037	1000000	1000000	10	kd_tree	1	1	0.029	NaN	0.983	0.984	0.728	0.014	3.801	0.089	See
10	KNeighborsClassifier_kd_tree	predict	0.768	0.016	1000000	1000	10	kd_tree	1	1	0.000	0.001	0.983	0.984	0.119	0.003	6.480	0.195	See
11	KNeighborsClassifier_kd_tree	predict	0.001	0.001	1000000	1	10	kd_tree	1	1	0.069	0.000	0.983	0.984	0.000	0.000	4.891	3.280	See
12	KNeighborsClassifier_kd_tree	fit	2.735	0.061	1000000	1000000	10	kd_tree	1	5	0.029	NaN	0.983	0.984	0.682	0.005	4.010	0.094	See
13	KNeighborsClassifier_kd_tree	predict	1.468	0.019	1000000	1000	10	kd_tree	1	5	0.000	0.001	0.983	0.984	0.223	0.005	6.584	0.159	See
14	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	5	0.069	0.000	0.983	0.984	0.000	0.000	2.506	1.523	See
15	KNeighborsClassifier_kd_tree	fit	2.703	0.028	1000000	1000000	10	kd_tree	1	100	0.030	NaN	0.983	0.984	0.725	0.015	3.727	0.085	See
16	KNeighborsClassifier_kd_tree	predict	4.758	0.079	1000000	1000	10	kd_tree	1	100	0.000	0.005	0.983	0.984	0.665	0.012	7.153	0.179	See
17	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	1	100	0.031	0.000	0.983	0.984	0.001	0.000	3.265	1.855	See
18	KNeighborsClassifier_kd_tree	fit	0.707	0.024	1000000	1000000	2	kd_tree	-1	1	0.023	NaN	0.983	0.984	0.470	0.007	1.507	0.056	See
19	KNeighborsClassifier_kd_tree	predict	0.034	0.003	1000000	1000	2	kd_tree	-1	1	0.000	0.000	0.983	0.984	0.001	0.001	34.916	20.514	See
20	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	1	0.006	0.000	0.983	0.984	0.000	0.000	12.974	7.040	See
21	KNeighborsClassifier_kd_tree	fit	0.702	0.015	1000000	1000000	2	kd_tree	-1	5	0.023	NaN	0.983	0.984	0.473	0.013	1.483	0.052	See
22	KNeighborsClassifier_kd_tree	predict	0.035	0.004	1000000	1000	2	kd_tree	-1	5	0.000	0.000	0.983	0.984	0.001	0.001	24.617	10.387	See
23	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	5	0.006	0.000	0.983	0.984	0.000	0.000	14.910	8.850	See
24	KNeighborsClassifier_kd_tree	fit	0.699	0.016	1000000	1000000	2	kd_tree	-1	100	0.023	NaN	0.983	0.984	0.494	0.060	1.414	0.173	See
25	KNeighborsClassifier_kd_tree	predict	0.054	0.003	1000000	1000	2	kd_tree	-1	100	0.000	0.000	0.983	0.984	0.007	0.001	8.104	1.018	See
26	KNeighborsClassifier_kd_tree	predict	0.006	0.007	1000000	1	2	kd_tree	-1	100	0.003	0.000	0.983	0.984	0.000	0.000	27.982	38.634	See
27	KNeighborsClassifier_kd_tree	fit	0.712	0.020	1000000	1000000	2	kd_tree	1	1	0.022	NaN	0.983	0.984	0.473	0.005	1.507	0.045	See
28	KNeighborsClassifier_kd_tree	predict	0.030	0.002	1000000	1000	2	kd_tree	1	1	0.001	0.000	0.983	0.984	0.001	0.000	29.972	14.552	See
29	KNeighborsClassifier_kd_tree	predict	0.001	0.001	1000000	1	2	kd_tree	1	1	0.016	0.000	0.983	0.984	0.000	0.000	6.562	4.866	See
30	KNeighborsClassifier_kd_tree	fit	0.703	0.009	1000000	1000000	2	kd_tree	1	5	0.023	NaN	0.983	0.984	0.477	0.015	1.474	0.051	See
31	KNeighborsClassifier_kd_tree	predict	0.032	0.002	1000000	1000	2	kd_tree	1	5	0.000	0.000	0.983	0.984	0.001	0.000	27.976	8.995	See
32	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	0.019	0.000	0.983	0.984	0.000	0.000	5.607	3.098	See
33	KNeighborsClassifier_kd_tree	fit	0.697	0.011	1000000	1000000	2	kd_tree	1	100	0.023	NaN	0.983	0.984	0.471	0.012	1.480	0.046	See
34	KNeighborsClassifier_kd_tree	predict	0.056	0.004	1000000	1000	2	kd_tree	1	100	0.000	0.000	0.983	0.984	0.007	0.001	8.132	1.449	See
35	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	0.018	0.000	0.983	0.984	0.000	0.000	5.085	2.837	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.601	0.010	1000000	1000000	2	full	k-means++	30	3	1	0.0	30	0.799	NaN	0.002	30	0.002	0.293	0.007	2.054	0.059	See
1	KMeans_tall	predict	0.002	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	30	0.010	0.0	0.002	30	0.002	0.000	0.000	7.164	2.715	See
2	KMeans_tall	predict	0.002	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	30	0.011	0.0	0.002	30	0.002	0.001	0.002	1.995	4.538	See
3	KMeans_tall	fit	0.497	0.009	1000000	1000000	2	full	random	30	3	1	0.0	30	0.966	NaN	0.002	30	0.002	0.261	0.005	1.905	0.048	See
4	KMeans_tall	predict	0.002	0.000	1000000	1000	2	full	random	30	3	1	0.0	30	0.010	0.0	0.002	30	0.002	0.000	0.000	3.850	1.290	See
5	KMeans_tall	predict	0.001	0.000	1000000	1	2	full	random	30	3	1	0.0	30	0.011	0.0	0.002	30	0.002	0.000	0.000	8.112	3.452	See
6	KMeans_tall	fit	6.833	0.073	1000000	1000000	100	full	k-means++	30	3	1	0.0	30	3.512	NaN	0.002	30	0.002	3.867	0.035	1.767	0.025	See
7	KMeans_tall	predict	0.002	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	30	0.461	0.0	0.002	30	0.002	0.000	0.000	5.497	1.753	See
8	KMeans_tall	predict	0.001	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	30	0.536	0.0	0.002	30	0.002	0.000	0.000	8.676	3.930	See
9	KMeans_tall	fit	6.396	0.066	1000000	1000000	100	full	random	30	3	1	0.0	30	3.753	NaN	0.002	30	0.002	3.638	0.057	1.758	0.033	See
10	KMeans_tall	predict	0.002	0.000	1000000	1000	100	full	random	30	3	1	0.0	30	0.449	0.0	0.002	30	0.002	0.000	0.000	4.155	1.724	See
11	KMeans_tall	predict	0.001	0.000	1000000	1	100	full	random	30	3	1	0.0	30	0.539	0.0	0.002	30	0.002	0.000	0.000	4.679	2.090	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.314	0.013	10000	10000	2	full	k-means++	30	300	1	0.0	28	0.014	NaN	0.002	25	0.006	0.140	0.008	2.250	0.152	See
1	KMeans_short	predict	0.002	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	28	0.008	0.0	0.002	25	0.006	0.001	0.000	2.668	1.006	See
2	KMeans_short	predict	0.002	0.000	10000	1	2	full	k-means++	30	300	1	0.0	28	0.010	0.0	0.002	25	0.006	0.000	0.000	9.075	4.094	See
3	KMeans_short	fit	0.128	0.003	10000	10000	2	full	random	30	300	1	0.0	30	0.037	NaN	0.002	30	0.006	0.072	0.004	1.790	0.118	See
4	KMeans_short	predict	0.002	0.001	10000	1000	2	full	random	30	300	1	0.0	30	0.007	0.0	0.002	30	0.006	0.001	0.000	2.824	1.183	See
5	KMeans_short	predict	0.001	0.000	10000	1	2	full	random	30	300	1	0.0	30	0.011	0.0	0.002	30	0.006	0.000	0.000	8.514	3.961	See
6	KMeans_short	fit	1.003	0.033	10000	10000	100	full	k-means++	30	300	1	0.0	16	0.128	NaN	0.002	24	0.006	0.590	0.041	1.700	0.130	See
7	KMeans_short	predict	0.003	0.001	10000	1000	100	full	k-means++	30	300	1	0.0	16	0.263	0.0	0.002	24	0.006	0.002	0.000	1.982	0.609	See
8	KMeans_short	predict	0.002	0.000	10000	1	100	full	k-means++	30	300	1	0.0	16	0.508	0.0	0.002	24	0.006	0.000	0.000	7.433	3.715	See
9	KMeans_short	fit	0.324	0.039	10000	10000	100	full	random	30	300	1	0.0	21	0.518	NaN	0.002	23	0.006	0.296	0.040	1.096	0.198	See
10	KMeans_short	predict	0.003	0.001	10000	1000	100	full	random	30	300	1	0.0	21	0.238	0.0	0.002	23	0.006	0.002	0.001	2.076	0.837	See
11	KMeans_short	predict	0.002	0.000	10000	1	100	full	random	30	300	1	0.0	21	0.518	0.0	0.002	23	0.006	0.000	0.000	7.023	2.821	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	throughput	latency	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	14.759	0.269	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	[-0.07994432]	NaN	0.32	14.526	0.030	1.016	0.019	See
1	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	2.1398418758814652	0.0	0.32	0.000	0.000	0.885	0.512	See
2	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	7.82015214239195	0.0	0.32	0.000	0.000	0.420	0.273	See
3	LogisticRegression	fit	1.267	0.021	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[29]	[-1.55846835]	NaN	0.32	1.162	0.032	1.091	0.035	See
4	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[29]	3.4293835067229668	0.0	0.32	0.004	0.001	0.570	0.090	See
5	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[29]	61.806559355642875	0.0	0.32	0.001	0.000	0.152	0.081	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	throughput	latency	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	0.269	0.005	1000	1000	10000	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.297	NaN	1.0	0.278	0.005	0.968	0.026	See
1	Ridge	predict	0.011	0.001	1000	1000	10000	1.0	True	True	NaN	deprecated	NaN	auto	0.001	7.563	0.0	1.0	0.017	0.001	0.615	0.058	See
2	Ridge	predict	0.000	0.000	1000	1	10000	1.0	True	True	NaN	deprecated	NaN	auto	0.001	795.468	0.0	1.0	0.000	0.000	0.644	0.438	See
3	Ridge	fit	1.140	0.035	1000000	1000000	100	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.702	NaN	1.0	0.320	0.006	3.559	0.128	See
4	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	deprecated	NaN	auto	0.001	2.584	0.0	1.0	0.000	0.000	1.063	0.743	See
5	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	deprecated	NaN	auto	0.001	4.524	0.0	1.0	0.000	0.000	1.067	0.574	See

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.2",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}